Project Summary/Abstract Despite its efficacy in severe depression, electroconvulsive therapy (ECT) is a medically intensive procedure with medical risk associated with general anesthesia and additional risk of transient and persistent/permanent cognitive difficulties. Although ECT is the most effective known somatic treatment for major depression, its response rate is estimated to be 50-70% in the general psychiatric practice, and a system to predict response would be of significant medical value. Many predictors of response to ECT have been proposed, but to date, have manifested only weak potential to discriminate which depressed patients are likely to benefit from ECT. Although the mechanisms by which ECT exerts its therapeutic effects in major depression are unknown, the functional neural circuitry (FC) underlying major depression and its treatment with ECT have become increasingly better defined. This represents an unexplored opportunity to develop a FC biomarker upon which to base recommendation of ECT and monitor the need for relapse prevention post ECT as well. To our knowledge, this is the first attempt to address this need for a predictive biomarker using functional connectivity. In the current R21 proposal, we propose to study whether a predictive model of brain functional connectivity patterns determined by fMRI, and trained on a pilot sample of depressed patients undergoing ECT can predict response to ECT and monitor relapse after response to ECT to a degree that is clinically useful in a larger sample. This is a prospective observation of depressed inpatients scheduled to undergo ECT. Participants will be examined with rsfMRI before the treatment course and once it is complete. We will test predictive analytic techniques developed in a prior pilot rsfMRI to see whether they discriminate responders from non-responders in this larger sample. In the follow-up period, we will use the same techniques to explore whether they can identify those patients at risk for relapse. The ultimate goal is to develop an effective biomarker to predict which patients could benefit from ECT as well as to monitor potential relapse and the need for further intervention.